The mission of link prediction is to predict the connection possibility of nodes that have not yet connected in the network, including the recovery of missing links and the formation of future links. The major difference of the aforementioned tasks is that the latter one mainly focuses on dynamic networks, which means links in those networks emerge at different time.
For example, for protein networks as static data[1]}, due to the insufficiency of our empirical knowledge, prediction of two proteins' interaction can be thought as restoration of missing links. Static link prediction focuses on the completeness of the graph, while dynamic link prediction mainly predicts the formation of future links in order to simulate network evolution. It is well established that networks are highly dynamic objects with inherent dynamic properties[2]}. Temporal link prediction aims to capture those properties in dynamic networks. It intends to extract the implicit driving force in the network and achieve the goal of network evolution analysis[3]}. The most important application of it is in recommender systems[4]}, which have been widely used in many fields, such as e-commerce, social network and other scenarios[5]}, [6]}.
